Resources

This page contains online book resources for instructors and students. You can contact us via email if you have any questions.

Solutions Manual

Solutions are available for Instructors. Please email us from an official university/academic email address to request the solutions manual (please also provide a URL to your official academic webpage/course page to verify that you are a faculty member).

Lecture Videos

You can access the lecture videos for the data mining course offered at RPI in Fall 2019.

You can also access the lectures in Portugese at the Youtube Channel (you can also change the language under Settings to generate closed captioning in English or your language of choice.)

Datasets

The datasets used for the examples in the book can be downloaded here: Datasets.zip

Slides

Complete latex sources for all slides are available. Below we include the PDF and PPT versions.

PART I. DATA ANALYSIS FOUNDATIONS

1 Data Mining and Analysis: Chap1 PDF, Chap1 PPT
2 Numeric Attributes: Chap2 PDF, Chap2 PPT
3 Categorical Attributes: Chap3 PDF, Chap3 PPT
4 Graph Data: Chap4 PDF, Chap4 PPT
5 Kernel Methods: Chap5 PDF, Chap5 PPT
6 High-dimensional Data: Chap6 PDF, Chap6 PPT
7 Dimensionality Reduction: Chap7 PDF, Chap7 PPT

PART II. FREQUENT PATTERN MINING

8 Itemset Mining: Chap8 PDF, Chap8 PPT
9 Summarizing Itemsets: Chap9 PDF, Chap9 PPT
10 Sequence Mining: Chap10 PDF, Chap10 PPT
11 Graph Pattern Mining: Chap11 PDF, Chap11 PPT
12 Pattern and Rule Assessment: Chap12 PDF, Chap12 PPT

PART III. CLUSTERING

13 Representative-based Clustering: Chap13 PDF, Chap13 PPT
14 Hierarchical Clustering: Chap14 PDF, Chap14 PPT
15 Density-based Clustering: Chap15 PDF, Chap15 PPT
16 Spectral and Graph Clustering: Chap16 PDF, Chap16 PPT
17 Clustering Validation: Chap17 PDF, Chap17 PPT

PART IV. CLASSIFICATION

18 Probabilistic Classification: Chap18 PDF, Chap18 PPT
19 Decision Tree Classifier: Chap19 PDF, Chap19 PPT
20 Linear Discriminant Analysis: Chap20 PDF, Chap20 PPT
21 Support Vector Machines: Chap21 PDF, Chap21 PPT
22 Classification Assessment: Chap22 PDF, Chap22 PPT

PART V. REGRESSION

23 Linear Regression: Chap23 PDF, Chap23 PPT
24 Logistic Regression: Chap24 PDF, Chap24 PPT
25 Neural Networks: Chap25 PDF, Chap25 PPT
26 Deep Learning: Chap26 PDF, Chap26 PPT
27 Regression Evaluation: Chap27 PDF, Chap27 PPT

Implementation-based Projects

Implementation-based Projects Here are some implementation-based project ideas. You can use python or R, or any other language/software of your choice.